I am trying to turn off the display of plot in R.
I read Disable GUI, graphics devices in R but the only solution given is to write the plot to a file.
What if I don't want to pollute the workspace and what if I don't have write permission ?
I tried options(device=NULL) but it didn't work.
The context is the package NbClust : I want what NbClust() returns but I do not want to display the plot it does.
Thanks in advance !
edit : Here is a reproducible example using data from the rattle package :)
data(wine, package="rattle")
df <- scale (wine[-1])
library(NbClust)
# This produces a graph output which I don't want
nc <- NbClust(df, min.nc=2, max.nc=15, method="kmeans")
# This is the plot I want ;)
barplot(table(nc$Best.n[1,]),
xlab="Numer of Clusters", ylab="Number of Criteria",
main="Number of Clusters Chosen by 26 Criteria")
You can wrap the call in
pdf(file = NULL)
and
dev.off()
This sends all the output to a null file which effectively hides it.
Luckily it seems that NbClust is one giant messy function with some other functions in it and lots of icky looking code. The plotting is done in one of two places.
Create a copy of NbClust:
> MyNbClust = NbClust
and then edit this function. Change the header to:
MyNbClust <-
function (data, diss = "NULL", distance = "euclidean", min.nc = 2,
max.nc = 15, method = "ward", index = "all", alphaBeale = 0.1, plotetc=FALSE)
{
and then wrap the plotting code in if blocks. Around line 1588:
if(plotetc){
par(mfrow = c(1, 2))
[etc]
cat(paste(...
}
and similarly around line 1610. Save. Now use:
nc = MyNbClust(...etc....)
and you see no plots unless you add plotetc=TRUE.
Then ask the devs to include your patch.
Related
I am trying to plot the GMM of my dataset using the Mclust package in R. While the plotting is a success, I do not want points to show in the final plot, just the ellipses. For a reference, here is the plot I have obtained:
GMM Plot
But, I want the resulting plot to have only the ellipses, something like this:
GMM desired plot
I have been looking at the Mclust plot page in: https://rdrr.io/cran/mclust/man/plot.Mclust.html and looking at the arguments of the function, I see there is a scope of adding other graphical parameters. Looking at the documentation of the plot function, there is a parameter called type = 'n' which might help to do what I want but when I write it, it produces the following error:
Error in plot.default(data[, 1], data[, 2], type = "n", xlab = xlab, ylab = ylab, :
formal argument "type" matched by multiple actual arguments
For reference, this is the code I used for the first plot:
library(mclust)
Data1_2 <- Mclust(Data, G=15)
summary(Data1_2, parameters = TRUE, classification = TRUE)
plot(Data1_2, what="classification")
The code I tried using for getting the graph below is:
Data1_4 <- Mclust(Data, G=8)
summary(Data1_4, parameters = TRUE, classification = TRUE)
plot(Data1_4, what="classification", type = "n")
Any help on this matter will be appreciated. Thanks!
If you look under the source code of plot.Mclust, it calls plot.Mclust.classification which in turn calls coordProj for the dot and ellipse plot. Inside this function, the size is controlled by the option CEX= and shape PCH=.
So for your purpose, do:
library(mclust)
clu = Mclust(iris[,1:4], G = 3, what="classification")
plot(clu,what="classification",CEX=0)
I'm trying to trouble shoot why Drake plots are not showing up with readd() - the rest of the pipeline seem's to have worked though.
Not sure if this is caused by minfi::densityPlot or some other reason; my thoughts are the later as it's also not working for the barplot function which is base R.
In the RMarkdown report I have readd(dplot1) etc. in the chunks but the output is NULL
This is the code I have in my R/setup.R file:
library(drake)
library(tidyverse)
library(magrittr)
library(minfi)
library(DNAmArray)
library(methylumi)
library(RColorBrewer)
library(minfiData)
pkgconfig::set_config("drake::strings_in_dots" = "literals") # New file API
# Your custom code is a bunch of functions.
make_beta <- function(rgSet){
rgSet_betas = minfi::getBeta(rgSet)
}
make_filter <- function(rgSet){
rgSet_filtered = DNAmArray::probeFiltering(rgSet)
}
This is my R/plan.R file:
# The workflow plan data frame outlines what you are going to do
plan <- drake_plan(
baseDir = system.file("extdata", package = "minfiData"),
targets = read.metharray.sheet(baseDir),
rgSet = read.metharray.exp(targets = targets),
mSetSq = preprocessQuantile(rgSet),
detP = detectionP(rgSet),
dplot1 = densityPlot(rgSet, sampGroups=targets$Sample_Group,main="Raw", legend=FALSE),
dplot2 = densityPlot (getBeta (mSetSq), sampGroups=targets$Sample_Group, main="Normalized", legend=FALSE),
pal = RColorBrewer::brewer.pal (8,"Dark2"),
dplot3 = barplot (colMeans (detP[,1:6]), col=pal[ factor (targets$Sample_Group[1:6])], las=2, cex.names=0.8, ylab="Mean detection p-values"),
report = rmarkdown::render(
knitr_in("report.Rmd"),
output_file = file_out("report.html"),
quiet = TRUE
)
)
After using make(plan) it looks like everything ran smoothly:
config <- drake_config(plan)
vis_drake_graph(config)
I am able to use loadd() to load the objects needed for one of these plots and then make the plots, like this:
loadd(rgSet)
loadd(targets)
densityPlot(rgSet, sampGroups=targets$Sample_Group,main="Raw", legend=FALSE)
But the readd() command doesn't work?
The output in the .html for dplot3 looks weird...
Fortunately, this is expected behavior. drake targets are return values of commands, and so the value of dplot3 is supposed to be the return value of barplot(). The return value of barplot() is actually not a plot. The "Value" section of the help file (?barplot) explains the return value.
A numeric vector (or matrix, when beside = TRUE), say mp, giving the coordinates of all the bar midpoints drawn, useful for adding to the graph.
If beside is true, use colMeans(mp) for the midpoints of each group of bars, see example.
So what is going on? As with most base graphics functions, the plot from barplot() is actually a side effect. barplot() sends the plot to a graphics device and then returns something else to the user.
Have you considered ggplot2? The return value of ggplot() is actually a plot object, which is more intuitive. If you want to stick with base graphics, maybe you could save the plot to an output file.
plan <- drake_plan(
...,
dplot3 = {
pdf(file_out("dplot3.pdf"))
barplot(...)
dev.off()
}
)
I'm using a function in R able to analyse my data and produce several plots.
The function is "snpzip" from adegenet package.
I would like to save automatically the three plots that the function produces as part of the output. Do you have any suggestion on how to do it?
I want to point to the fact that I know how to save a single plot, for instance with png or pdf followed by dev.off(). My problem is that when I run snpzip(snps, phen, method = "centroid"), the outcomes are three plots (which I would like to save).
I report here the same example as in the "adegenet" package:
simpop <- glSim(100, 10000, n.snp.struc = 10, grp.size = c(0.3,0.7),
LD = FALSE, alpha = 0.4, k = 4)
snps <- as.matrix(simpop)
phen <- simpop#pop
outcome <- snpzip(snps, phen, method = "centroid")
If you use a filename with a C integer format in it, then R will substitute the page number for that part of the name, generating multiple files. For example,
png("page%d.png")
plot(1)
plot(2)
plot(3)
dev.off()
will generate 3 files, page1.png, page2.png, and page3.png. For pdf(), you also need onefile=FALSE:
pdf("page%d.pdf", onefile = FALSE)
plot(1)
plot(2)
plot(3)
dev.off()
I was reading through this blog post on R-bloggers and I'm confused by the last section of the code and can't figure it out.
http://www.r-bloggers.com/self-organising-maps-for-customer-segmentation-using-r/
I've attempted to recreate this with my own data. I have 5 variables that follow an exponential distribution with 2755 points.
I am fine with and can plot the map that it generates:
plot(som_model, type="codes")
The section of the code I don't understand is the:
var <- 1
var_unscaled <- aggregate(as.numeric(training[,var]),by=list(som_model$unit.classif),FUN = mean, simplify=TRUE)[,2]
plot(som_model, type = "property", property=var_unscaled, main = names(training)[var], palette.name=coolBlueHotRed)
As I understand it, this section of the code is suppose to be plotting one of the variables over the map to see what it looks like but this is where I run into problems. When I run this section of the code I get the warning:
Warning message:
In bgcolors[!is.na(showcolors)] <- bgcol[showcolors[!is.na(showcolors)]] :
number of items to replace is not a multiple of replacement length
and it produces the plot:
Which just some how doesn't look right...
Now what I think it has come down to is the way the aggregate function has re-ordered the data. The length of var_unscaled is 789 and the length of som_model$data, training[,var] and unit.classif are all of length 2755. I tried plotting the aggregated data, the result was no warning but an unintelligible graph (as expected).
Now I think it has done this because unit.classif has a lot of repeated numbers inside it and that's why it has reduced in size.
The question is, do I worry about the warning? Is it producing an accurate graph? What exactly is the "Property"'s section looking for in the plot command? Is there a different way I could "Aggregate" the data?
I think that you have to create the palette color. If you put the argument
coolBlueHotRed <- function(n, alpha = 1) {rainbow(n, end=4/6, alpha=alpha)[n:1]}
and then try to get a plot, for example
plot(som_model, type = "count", palette.name = coolBlueHotRed)
the end is succesful.
This link can help you: http://rgm3.lab.nig.ac.jp/RGM/R_rdfile?f=kohonen/man/plot.kohonen.Rd&d=R_CC
I think that not all of the cells on your map have points inside.
You have 30 by 30 map and about 2700 points. In average it's about 3 points per cell. With high probability some cells have more than 3 points and some cells are empty.
The code in the post on R-bloggers works well when all of the cells have points inside.
To make it work on your data try change this part:
var <- 1
var_unscaled <- aggregate(as.numeric(training[, var]), by = list(som_model$unit.classif), FUN = mean, simplify = TRUE)[, 2]
plot(som_model, type = "property", property = var_unscaled, main = names(training)[var], palette.name = coolBlueHotRed)
with this one:
var <- 1
var_unscaled <- aggregate(as.numeric(data.temp[, data.classes][, var]),
by = list(som_model$unit.classif),
FUN = mean,
simplify = T)
v_u <- rep(0, max(var_unscaled$Group.1))
v_u[var_unscaled$Group.1] <- var_unscaled$x
plot(som_model,
type = "property",
property = v_u,
main = colnames(data.temp[, data.classes])[var],
palette.name = coolBlueHotRed)
Hope it helps.
Just add these functions to your script:
coolBlueHotRed <- function(n, alpha = 1) {rainbow(n, end=4/6, alpha=alpha)[n:1]}
pretty_palette <- c("#1f77b4","#ff7f0e","#2ca02c", "#d62728","#9467bd","#8c564b","#e377c2")
I am trying to plot two regression summaries side-by-side with one centered title. Each regression summary is generated by plot.rqs() and amounts to a set of 9 plots.
I've tried using par(mfrow=c(1,2)) already, but as I learnt from Paul Murrel's (2006) book, high-level functions like plot.rqs() or pairs() save the graphics state before drawing and then restore the graphics state once completed, so that pre-emptive calls to par() or layout() can't help me. plot.rqs() doesn't have a 'panel' function either.
It seems that the only way to achieve the result is to modify the plot.rqs() function to get a new function, say modified.plot.rqs(), and then run
par(mfrow=c(1,2))
modified.plot.rqs(summary(fit1))
modified.plot.rqs(summary(fit2))
par(mfrow=c(1,1))
From there I might be able to work out how to add an overall title to the image using layout(). Does anyone know how to create a modified.plot.rqs() function that could be used in this way?
Thanks
You can patch a function as follows:
use dput and capture.output to retrieve
the code of the function, as a string;
change it as you want (here, I just replace each occurrence of par
with a function that does nothing);
finally evaluate the result to produce a new function.
library(quantreg)
a <- capture.output(dput(plot.summary.rqs))
b <- gsub("^\\s*par\\(", "nop(", a)
nop <- function(...) {}
my.plot.summary.rqs <- eval(parse(text=b))
First we generate an example object, fm . Then we copy plot.rqs and use trace on the copy to insert par <- list at top effectively nullifying any use of par within the function. Then we do the same with plot.summary.rqs. Finally we test it out with our own par:
library(quantreg)
example(plot.rqs) # fm to use in example
# plot.rqs
plot.rqs <- quantreg::plot.rqs
trace("plot.rqs", quote(par <- list), print = FALSE)
# plot.summary.rqs
plot.summary.rqs <- quantreg::plot.summary.rqs
trace("plot.summary.rqs", quote(par <- list), print = FALSE)
# test it out
op <- par(mfrow = c(2, 2))
plot(summary(fm))
plot(fm)
title("My Plots", outer = TRUE, line = -1)
par(op)
EDIT: added plot.summary.rqs.